Donald Loveland

27 papers A* 3A 2Journal 18Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Plain Transformers are Surprisingly Powerful Link Predictors. CoRR Quang Truong, Yu Song, Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Jiliang Tang
2025	J	`jnl`	Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendation. CoRR Liam Collins, Bhuvesh Kumar, Clark Mingxuan Ju, Tong Zhao, Donald Loveland, Leonardo Neves, Neil Shah
2025	J	`jnl`	Glance for Context: Learning When to Leverage LLMs for Node-Aware GNN-LLM Fusion. CoRR Donald Loveland, Yao-An Yang, Danai Koutra
2025	A*	`conf`	GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems. WWW Xinyi Wu, Donald Loveland, Runjin Chen, Yozen Liu, Xin Chen, Leonardo Neves, Ali Jadbabaie, Mingxuan Ju, Neil Shah, Tong Zhao
2025	A	`conf`	MAGNET: A Multi-Agent Graph Neural Network for Efficient Bipartite Task Assignment. AAMAS Donald Loveland, James Usevitch, Zachary Serlin, Danai Koutra, Rajmonda Caceres
2025	—	`conf`	On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective. KDD (2) Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Danai Koutra
2025	J	`jnl`	On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective. CoRR Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Danai Koutra
2025	A*	`conf`	Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank. WWW Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, Mingxuan Ju
2025	A	`conf`	Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks. SDM Donald Loveland, Danai Koutra
2024	J	`jnl`	GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems. CoRR Xinyi Wu, Donald Loveland, Runjin Chen, Yozen Liu, Xin Chen, Leonardo Neves, Ali Jadbabaie, Clark Mingxuan Ju, Neil Shah, Tong Zhao
2024	J	`jnl`	Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank. CoRR Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, Mingxuan Ju
2024	J	`jnl`	Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks. CoRR Donald Loveland, Danai Koutra
2023	—	`conf`	Network Design Through Graph Neural Networks: Identifying Challenges and Improving Performance. COMPLEX NETWORKS (1) Donald Loveland, Rajmonda Caceres
2023	J	`jnl`	Network Design through Graph Neural Networks: Identifying Challenges and Improving Performance. CoRR Donald Loveland, Rajmonda Caceres
2023	—	`conf`	On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. LoG Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
2023	J	`jnl`	On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. CoRR Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
2022	J	`jnl`	FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing. CoRR Donald Loveland, Jiayi Pan, Aaresh Farrokh Bhathena, Yiyang Lu
2022	A*	`conf`	How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. KDD Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra
2022	J	`jnl`	On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. CoRR Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
2022	J	`jnl`	Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning. CoRR Ioannis C. Tsaknakis, Bhavya Kailkhura, Sijia Liu, Donald Loveland, James Diffenderfer, Anna Maria Hiszpanski, Mingyi Hong
2021	J	`jnl`	Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning. J. Chem. Inf. Model. Phan Nguyen, Donald Loveland, Joanne Taery Kim, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han
2021	J	`jnl`	Reliable Graph Neural Network Explanations Through Adversarial Training. CoRR Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, Yong Han
2020	J	`jnl`	Actionable Attribution Maps for Scientific Machine Learning. CoRR Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han
2020	J	`jnl`	Automated Identification of Molecular Crystals' Packing Motifs. J. Chem. Inf. Model. Donald Loveland, Bhavya Kailkhura, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han
2020	J	`jnl`	Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design. CoRR Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han
2019	—	`conf`	Generative Counterfactual Introspection for Explainable Deep Learning. GlobalSIP Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han
2019	J	`jnl`	Generative Counterfactual Introspection for Explainable Deep Learning. CoRR Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han